arXiv subjects

Rajni Chahal

Publications and source records attributed to Rajni Chahal.

10 recordsLinked to original sources

arXiv2026

Cesium Clustering and Fluoroberyllate Network Disruption in FLiBe: A Total Scattering and Molecular Dynamics Study

Several next-generation fission reactor designs employ molten salts such as FLiBe (2LiF-BeF$_2$), with some concepts using fuel dissolved directly in the salt. During operation, fission products such as cesium will accumulate in the salt mixture, potentially leading to an evolution of the thermophysical properties underpinned by the atomic structure. To understand the structural perturbations in FLiBe with 5 mol% CsF, we conducted X-ray and neutron diffraction measurements, refined empirical potential structure refinement (EPSR) models against the experimental data, and compared the resulting structure with neural network molecular dynamics (NNMD) simulations. Comparisons of the EPSR and NNMD structures distinguishes features constrained by the scattering data from those that remain model dependent. The new Cs-bearing correlations account for the changes in the total structure factor and pair-distribution function, while the FLiBe correlations remain minimally altered. We find that Cs slightly disrupts the intermediate-range fluoroberyllate network, increasing the fraction of free fluorine ions, while the local coordination remains largely unchanged. The Cs ions within FLiBe cluster extensively, with BeF$_4^{2-}$ tetrahedra bridging neighboring cesium environments. In contrast to the minor structural perturbations in the liquid, the addition of 5 mol% CsF suppressed the formation of the crystalline Li2BeF4 phase at room temperature, with the phase appearing only above 180C upon heating. These experimentally constrained structural features provide a benchmark for atomistic models used to predict the behavior and properties of fission-product-containing FLiBe.

cond-mat.mtrl-sci↗

arXiv2025

Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research

The AI for Nuclear Energy workshop at Oak Ridge National Laboratory evaluated the potential of Large Language Models (LLMs) to accelerate fusion and fission research. Fourteen interdisciplinary teams explored diverse nuclear science challenges using ChatGPT, Gemini, Claude, and other AI models over a single day. Applications ranged from developing foundation models for fusion reactor control to automating Monte Carlo simulations, predicting material degradation, and designing experimental programs for advanced reactors. Teams employed structured workflows combining prompt engineering, deep research capabilities, and iterative refinement to generate hypotheses, prototype code, and research strategies. Key findings demonstrate that LLMs excel at early-stage exploration, literature synthesis, and workflow design, successfully identifying research gaps and generating plausible experimental frameworks. However, significant limitations emerged, including difficulties with novel materials designs, advanced code generation for modeling and simulation, and domain-specific details requiring expert validation. The successful outcomes resulted from expert-driven prompt engineering and treating AI as a complementary tool rather than a replacement for physics-based methods. The workshop validated AI's potential to accelerate nuclear energy research through rapid iteration and cross-disciplinary synthesis while highlighting the need for curated nuclear-specific datasets, workflow automation, and specialized model development. These results provide a roadmap for integrating AI tools into nuclear science workflows, potentially reducing development cycles for safer, more efficient nuclear energy systems while maintaining rigorous scientific standards.

physics.comp-ph↗

arXiv2024

Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic Potentials

Molten salts are promising candidates in numerous clean energy applications, where challenges in experimental methods limit knowledge of their safety-critical temperature-properties correlations. Herein, we developed and employed machine learning interatomic potentials (MLIP) to study AlCl3 molten salt across varied thermodynamic conditions. The MLIP accurately predicted the existence of Al2Cl6 dimers in this molten salt as informed by Raman spectra and neutron structure factor. The MLIP is validated using available experimental data for temperature correlations with viscosities, surface tension, as well as liquid and vapor densities evaluated from two-phase coexistence simulations. In doing so, we closely predicted the critical temperature and critical density compared to reported experimental values for AlCl3. The demonstrated approach for MLIP training in closely predicting phase equilibrium in this study can be useful towards screening nuclear reactors-relevant salt compositions, helping to mitigate safety concerns.

physics.chem-ph↗

arXiv2024

Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning

Optimally designing molten salt applications requires knowledge of their thermophysical properties, but existing databases are incomplete, and experiments are challenging. Ideal mixing and Redlich-Kister models are computationally cheap but lack either accuracy or generality. To address this, a transfer learning approach using deep neural networks (DNNs) is proposed, combining Redlich-Kister models, experimental data, and ab initio properties. The approach predicts molten salt density with high accuracy ($r^{2}$ > 0.99, MAPE < 1%), outperforming the alternatives.

cs.LG↗

arXiv2024

Uncertainty and Exploration of Deep Learning-based Atomistic Models for Screening Molten Salt Properties and Compositions

Due to extreme chemical, thermal, and radiation environments, existing molten salt property databases lack the necessary experimental thermal properties of reactor-relevant salt compositions. Meanwhile, simulating these properties directly is typically either computationally expensive or inaccurate. In recent years, deep learning (DL)-based atomistic simulations have emerged as a method for achieving both efficiency and accuracy. However, there remain significant challenges in assessing model reliability in DL models when simulating properties and screening new systems. In this work, structurally complex LiF-NaF-ZrF$_4$ salt is studied. We show that neural network (NN) uncertainty can be quantified using ensemble learning to provide a 95% confidence interval (CI) for NN-based predictions. We show that DL models can successfully extrapolate to new compositions, temperatures, and timescales, but fail for significant changes in density, which is captured by ensemble-based uncertainty predictions. This enables improved confidence in utilizing simulated data for realistic reactor conditions, and guidelines for training deployable DL models.

cond-mat.mtrl-sci↗

arXiv2024

Deep Learning Interatomic Potential Connects Molecular Structural Ordering to Macroscale Properties of Polyacrylonitrile (PAN) Polymer

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units are indispensable to advance the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers such as dipole-dipole interactions and hydrogen bonding, and analyzing their influence on molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIP) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures/properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen bonding and dipolar correlations, and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties such as density and elastic modulus are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in Hermans orientation factor. This study enables the ability to predict the structure-property relations for PAN and analogs with sustainable ab initio accuracy across scales.

cond-mat.mtrl-sci↗

arXiv2024

X-ray and molecular dynamics study of the temperature-dependent structure of molten NaF-ZrF4

The local atomic structure of NaF-ZrF$_4$ (53-47 mol%) molten system and its evolution with temperature are examined with x-ray scattering measurements and compared with $ab-initio$ and Neural Network-based molecular dynamics (NNMD) simulations in the temperature range 515-700 °C. The machine-learning enhanced NNMD calculations offer improved efficiency while maintaining accuracy at higher distances compared to ab-initio calculations. Looking at the evolution of the Pair Distribution Function with increasing temperature, a fundamental change in the liquid structure within the selected temperature range, accompanied by a slight decrease in overall correlation is revealed. NNMD calculations indicate the co-existence of three different fluorozirconate complexes: [ZrF$_6$]$^{2-}$, [ZrF$_7$]$^{3-}$, and [ZrF$_8$]$^{4-}$, with a temperature-dependent shift in the dominant coordination state towards a 6-coordinated Zr ion at 700°C. The study also highlights the metastability of different coordination structures, with frequent interconversions between 6 and 7 coordinate states for the fluorozirconate complex from 525 °C to 700 °C. Analysis of the Zr-F-Zr angular distribution function reveals the presence of both $"$edge-sharing$"$ and $"$corner-sharing$"$ fluorozirconate complexes with specific bond angles and distances in accord with previous studies, while the next-nearest neighbor cation-cation correlations demonstrate a clear preference for unlike cations as nearest-neighbor pairs, emphasizing non-random arrangement. These findings contribute to a comprehensive understanding of the complex local structure of the molten salt, providing insights into temperature-dependent preferences and correlations within the molten system.

cond-mat.mtrl-sci↗

arXiv2023

ab initio informed inelastic neutron scattering for time-resolved local dynamics in molten MgCl2

Ion dynamics that drive the transport and thermophysical properties of molten salts are poorly understood due to challenges in precisely quantifying the spatial and temporal fluctuations of specific ions in highly disordered systems. While the Van Hove correlation function (VHF) obtained from inelastic neutron scattering (INS) probes these dynamics directly, its interpretation is limited by the inherent species-averaging of experiments, which obscures analysis of key ion transport and solvation mechanisms. Here, ab initio molecular dynamics (AIMD) is used to model the VHF, unravel its partial contributions, and elucidate its underlying ionic transport mechanisms. Slow decorrelation is revealed for oppositely charged ions (Mg2+ and Cl-) caused by ion exchange across the solvation shell between adjoining ionocovalent complexes. Furthermore, transport coefficients are accurately recovered and connections between macroscopic properties and ion dynamics are revealed. This study demonstrates the potential of ab initio-informed VHF to resolve long-standing challenges in uncovering relationships between picosecond-scale ion dynamics, mechanisms, and emergent physical properties of molten salts.

cond-mat.mtrl-sci↗

arXiv2022

Transferable Deep Learning Potential Reveals Intermediate-Range Ordering Effects in LiF-NaF-ZrF4 Molten Salt

LiF-NaF-ZrF4 multicomponent molten salts are promising candidate coolants for advanced clean energy systems owing to their desirable thermophysical and transport properties. However, the complex structures enabling these properties, and their dependence on composition, is scarcely quantified due to limitations in simulating and interpreting experimental spectra of highly disordered, intermediate-ranged structures. Specifically, size-limited ab-initio simulation and accuracy-limited classical models used in the past, are unable to capture a wide range of fluctuating motifs found in the extended heterogeneous structures of liquid salt. This greatly inhibits our ability to design tailored compositions and materials. Here, accurate, efficient and transferable machine learning potentials are used to predict structures far beyond the first coordination shell in LiF-NaF-ZrF4. Neural networks trained at only eutectic compositions with 29% and 37% ZrF4 are shown to accurately simulate a wide range of compositions (11 to 40% ZrF4) with dramatically different coordination chemistries, while showing a remarkable agreement with theoretical and experimental Raman spectra. The theoretical Raman calculations further uncovered the previously unseen shift and flattening of bending band at ~250 cm-1 which validated the simulated extended-range structures as observed in compositions with higher than 29% ZrF4 content. In such cases, machine learning-based simulations capable of accessing larger time- and length-scales (beyond 17 Å) were critical for accurately predicting both structure and ionic diffusivities.

physics.chem-ph↗

arXiv2022

Short to Intermediate-range Structure , Transport , and Thermophysical Properties of LiF-NaF-ZrF4 Molten Salts

LiF-NaF-ZrF4 multicomponent molten salts are identified as promising candidates for coolant salts in molten salt reactors and advanced high-temperature reactors. Herein, ab-initio molecular dynamics (AIMD) calculations were performed and compared with available experimental data to assess the ability of polarizable ion models (PIM) to reproduce short to intermediate-range structure, transport and thermophysical properties of the LiF-NaF-ZrF4 salt mixtures. This study signifies the importance of accurate salt structure generation for accurate prediction of transport and thermophysical properties of multicomponent molten salts. Keywords: Multicomponent Molten salts, Ab-initio molecular dynamics, Polarizable ion model, Fluorozirconate chains, Neoteric liquids simulation, Ionic liquids, Thermophysical properties, Diffusion coefficients.

cond-mat.mtrl-sci↗